research-article

Frag-shells cube based on hierarchical dimension encoding tree

Authors:

Jianguo YaoAuthors Info & Claims

IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

Article No.: 2, Pages 1 - 9

https://doi.org/10.1145/3022227.3022229

Published: 05 January 2017 Publication History

Abstract

Pre-computation of data cube can greatly improve the performance of OLAP (online analytical processing). There are a lot of effective pre-computation methods of data cube. But in practice, appropriate pre-computation method for the characteristics of data set plays a crucial role in improving the efficiency of data cube pre-computation.

In view of the high-dimensional and hierarchical dimension of water census data characteristic, Frag-Shells cube based on hierarchical dimension encoding tree has been proposed in this paper. In order to improve the retrieval efficiency and reduce redundant information in hierarchical dimensions, we have proposed hierarchical dimension encoding tree (HDE-Tree) to index the hierarchical dimension in Frag-Shells cube. In order to increase the efficiency of cube construction, improved Frag-Shells cube calculation method has been used to compute the tuples of non-hierarchical dimension fragments. In order to compress the size of data cube, the TID-List compression method has been used to decrease the storage cost of inverted index in each tuple. Experiments show that the Frag-Shells cube based on hierarchical dimension encoding tree can reduce the construction time and storage cost of data cube which has high-dimensional and dimensions hierarchical figures.

References

[1]

Gray J, Chaudhuri S, Bosworth A, et al. Data Cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Mining and Knowledge Discovery 1.1(1997): 29--53.

Digital Library

[2]

Jiang F M, Pei J, Fu A W. Ix-cubes: iceberg cubes for data warehousing and olap on xml data. Sixteenth Acm Conference on Information and Knowledge Management 2007: 905--908.

Digital Library

[3]

Wang Z, Xu Y. Minimal Condensed Cube: data organization, fast computation, and incremental update. International Conference on Internet Computing in Science and Engineering 2008:60--67.

Digital Library

[4]

Lakshmanan L V. S., J. Pei, and J. Han. Quotient cube: how to summarize the semantics of a data cube. Proceedings of the 28th international conference on Very Large Data Bases VLDB Endowment, 2002:778--789.

Digital Library

[5]

Lakshmanan L V. S., J. Pei, and Y. Zhao. QC-Trees: An efficient summary structure for semantic OLAP. ACM SIGMOD International Conference on Management of Data, 2003:64--75.

Digital Library

[6]

Li X L, Han J W, Gonzalez H. High-dimensional OLAP: a minimal cubing approach. Proceedings of the Thirtieth International Conference on VLDB, 2004: 528--539.

Digital Library

[7]

Stockinger, Kurt. Bitmap indices for speeding up high-dimensional data analysis. Database and Expert Systems Applications. Springer Berlin Heidelberg, 2002:881--890.

Digital Library

[8]

Lomet D, Hong M, Nehme R, et al. Transaction time indexing with version compression. Proceedings of the VLDB Endowment 1.1 (2008): 870--881.

Digital Library

[9]

Augsten N, Böhlen M, Gamper J. An Incrementally Maintainable Index for Approximate Lookups in Hierarchical Data. International Conference on VLDB, 2006:247--258.

Digital Library

[10]

He H, Zhang Y, Ren J, et al. An improved OLAP join and aggregate algorithm based on dimension hierarchy. Fuzzy Systems and Knowledge Discovery, 2009. FSKD. Sixth International Conference on. Vol. 5. IEEE, 2009:169--172.

Digital Library

[11]

Lv Q, Josephson W, Wang Z, et al. Multi-probe LSH: efficient indexing for high-dimensional similarity search. International Conference on Very Large Data Bases, 2007:950--961.

Digital Library

[12]

He H, Zhang Y, Li L, et al. An improved storage algorithm for multi-dimensional data cube. Innovative Computing, Information and Control (ICICIC). IEEE, 2009:841--844.

Digital Library

[13]

Tripathy C R. Star-cube: A New Fault-tolerant Interconnection Topology for Massively Parallel Systems. Journal-Institution of Engineers Electronics and Telecommunications Engineering division, 2004: 83--92.

[14]

Zhu K, Wan D, Cheng X. Computation of Data Cube in Analysis of Water Census Results. Computer and Digital Engineering, 2014, 42(9): 1591--1594.

[15]

Hu K F, Chen L, Jie S, et al. A highly performance dimension hierarchy aggregate cube and its incremental update algorithm. International Conference on Machine Learning and Cybernetics. IEEE, 2005:2195 -- 2199.

[16]

Gong Z, Hu K, Da Q L. A grouping aggregation algorithm based on the dimension hierarchical encoding in data warehouse. Computer Information Systems and Industrial Management Applications, 2007. CISIM'07. 6th International Conference on. IEEE, 2007:135--142.

Digital Library

[17]

Qi G U, Hu K F, Chen L, et al. Research on Storage and Incremental Update Technology of Semantic Cube Based on Dimension Hierarchy. Application Research of Computers, 2007, 24(6):213--215.

[18]

LIU Y, Wang H, Liao G. Solution of Composite Index Problem by HB+ Tree. Computer Engineering, 2005, 31(17):46--48.

[19]

Li D, GAO Z, Qiu X, et al. An Improved Frag-Shells Algorithm for Data Cube Construction Based on Irrelevance of Data Dispersion. Intelligent Computation in Big Data Era. Springer Berlin Heidelberg, 2015:292--299.

[20]

Tang S S, Zhu Y L, Zhu K. Parallel computation of shell fragments cube Map/Reduce-based. Computer Engineering and Applications, 2015, 51(22):124--129.

Cited By

Zhao QZhu YWan DTang S(2018)A Closed Frag-Shells Cubing Algorithm on High Dimensional and Non-Hierarchical Data SetsProceedings of the 12th International Conference on Ubiquitous Information Management and Communication10.1145/3164541.3164585(1-8)Online publication date: 5-Jan-2018
https://dl.acm.org/doi/10.1145/3164541.3164585

Index Terms

Frag-shells cube based on hierarchical dimension encoding tree
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Online analytical processing engines
    2. Information integration
      1. Data warehouses

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cover image ACM Conferences

IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

January 2017

746 pages

ISBN:9781450348881

DOI:10.1145/3022227

Copyright © 2017 ACM.

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Published: 05 January 2017

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National Science and Technology Support Plan Project of China
the Ministry of Water Resources Public Welfare Industry Research Special Foundation of China

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IMCOM '17

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SIGAPP

IMCOM '17: The 11th International Conference on Ubiquitous Information Management and Communication

January 5 - 7, 2017

Beppu, Japan

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IMCOM '17 Paper Acceptance Rate 113 of 366 submissions, 31%;

Overall Acceptance Rate 213 of 621 submissions, 34%

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Cited By

Zhao QZhu YWan DTang S(2018)A Closed Frag-Shells Cubing Algorithm on High Dimensional and Non-Hierarchical Data SetsProceedings of the 12th International Conference on Ubiquitous Information Management and Communication10.1145/3164541.3164585(1-8)Online publication date: 5-Jan-2018
https://dl.acm.org/doi/10.1145/3164541.3164585

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